Full Transcript: Tian Yang on Leading Indicators and the Macro Setup

Causal Data, the Capital Cycle, and Why the Fed May Not Hike

Jack: Welcome to Excess Returns. I’m Jack Forehand, and I’m excited to be joined today by Tian Yang. Tian’s the head of research at Variant Perception and also the portfolio manager of the VPX ETF, and we’re gonna talk a little macro today. So, Tian, thank you for joining us.

Tian: Thanks for having me. Looking forward to it.

Jack: You guys do some awesome work. And you blend quantitative and qualitative frameworks, which is exactly what I like to do as well. So I’m really interested to dig into... We’re gonna dig into the economy in general, but we’re also gonna dig into the framework behind it and how you’re getting to the conclusions you guys are getting to.

But I want to start with a quote. You have a really great quote here that I think summarizes what you guys do, but I think is really relevant for what’s going on in the market today. And the quote is, “Data is easier to access and more available than ever before. The key now is how creatively you use these inputs and, most importantly, what you choose to leave out.”

And that last part was really important, I think, to me in the world of noise, is what you choose to leave out. So could you talk about what you mean by that statement?

Tian: Yeah. So I think what we’re implicitly trying to say here is that you have to think from first principles. What are the causal reasons that data is statistically meaningful? So, you know, we all want to build models, especially in this age of AI. You know, pretty soon we’ll have super powered AI to help us build models, right? So I think a lot of times though, even when you’re doing that and processing data, it’s easy to throw things to a black box, find something that backtests really well and want to use it.

I think from where we’re coming from, we think a lot about, is there something causal? Is there a real world reason that this thing has a relationship and it persists? So a very simple example we give is essentially the intuition behind the idea of even leading indicators in the first place, right? That there’s a certain sequence in which things happen in real world economies. You have to get a building permit before you can build a house. So, you know, if you keep an eye on building permits, that’ll give you a sense of when people want to build a house, right?

And there’s lots of these examples. So I think that’s probably more what we’re getting at, to think a lot about causal relationships, which actually just means sequencing. What moves first to then cause something else to move. Is that something that is, from first principles, likely to persist through time? And let’s build models around that, find data to proxy for those, and then from there obviously build up the analytical framework.

Jack: You referenced leading indicators in your answer, and I know that’s a big part of what you guys do. And, you know, many people tend to focus on sort of what the data is right now and maybe not necessarily what it’s going to be in the future. So can you just explain to me, what do you define a leading indicator as? What’s important in a leading indicator for you?

Tian: Yeah. So, you know, I think as investors we’ll look a lot at GDP, right? Or inflation or Fed policy. Well, she’s made a policy announcement. These are all what we would say is real-time coincident things that happen. They may or may not move the market. But often though, by the time these data points move, there’s a sequence of things that have happened ahead of time that you can actually track and look at.

And in a way, when we say leading indicators, it’s almost like instead of trying to get a crystal ball and forecast something, you’re just standing back and observing if the data’s shifting. So if we take that building permit example I gave through to its conclusion, it’s like, okay, let’s say we observe building permits start to surge. Right? This month, next month, and you get a few months in a row of building permits going up, then we know there’s gonna be a lot of construction activity, right?

Especially if it’s residential building permits, then you know they’ll build that house the next six, nine, 12 months. After that, people are gonna move in. Suddenly when they move in, what are they gonna do? They’re gonna take home mortgages, right? They’re gonna try and borrow. They’re gonna buy a new fridge. And there’s all this activity that comes afterwards as a sequence from observing this first turning point.

And the idea is to go through different parts of the economy and look for these potential shifts. So it tends to be more intuitive in traditional cyclical industries. So things like manufacturing, if you track order books, that’s very intuitive, right? If you track order books relative to inventory levels, again, that’s very intuitive. If industrial businesses suddenly start reporting a lot more new orders, and they also tell you their inventories are low, that’s a pretty good sign that there’s a lot of future activity to come that isn’t necessarily in the current GDP data, but can be in the future.

And so I think it’s kind of taking that idea through on the growth side, on the inflation side as well. And even on things like policy, right, where you want to get ahead of it, think about, okay, what are central bank policy mandates? Where is growth and inflation relative to their mandate? What’s the curve pricing in? And based on that, what’s likely to be their policy response function, right? It’s just a lot of these lead lag relationships that we tend to anchor our analysis and models on.

Jack: Have you seen any shift in how leading indicators work post-pandemic? You know, when people talk about these, they’re talking about different leading indicators than what you’re using. But a lot of the traditional leading indicators you’ll see all over CNBC, many people argue have not worked. They’ve been predicting recession for a lot, a lot since 2020. I mean, have you seen a shift in how leading indicators work post-pandemic? Did something change there?

Tian: Yeah. Definitely. So I guess you’re talking about things like the Conference Board LEIs, right? And famously, some of them have stock market as a leading indicator—

Jack: Yeah. Like, anything that—

Tian: —and software search, you know?

Jack: Yeah, anything at a high level you see on CNBC that brings together these leading indicators that predict recession, even things like yield curve inversion or things like that. A lot of that stuff seems to have not worked as well post-2020.

Tian: Yeah. So I think the way we do it is we have kind of a multi-stage process. So we actually have what we would call a causal discovery process. So we run a bunch of algorithms to try to understand at any given point in time, which is a lead indicator that’s been predictive at recent turning points, and we’ll just essentially overweight those, so that your model can adapt.

I think the reason people are frustrated with those traditional lead indicators is a lot of the models are static, the inputs stay the same, and the coefficients stay the same. And so clearly, we’re in a world where there’s more information, more need to adapt. So the idea is that you want your inputs to change and your coefficients you assign to those inputs to change as well through time.

But I don’t think the necessary first principle is wrong, right? Yield curves do have a sound fundamental reason. It’s just you have to try to be sensitive to when they might work less well. So yield curves in a traditional credit cycle, when inflation’s not a problem, when fiscal policy is not a problem, and central banks hike or cut interest rates in response to worries about growth and inflation, they work really well.

If we live in a fiscal world, a world where sovereignty dominates, there’s US-China competition, right? Where governments are again involved in FX markets to help the Japanese backstop the yen, right? There’s a number of these other things that come in that, if you’re looking under the hood, you’ll start to observe these things become less effective. It doesn’t mean they won’t go back to working at some point in the future. It just means right now the primary kind of mechanisms in which the macro economy operates have changed.

Jack: Is it almost like you have to look at these leading indicators as a mosaic, and at certain points in time, certain leading indicators are more important than other leading indicators?

Tian: Yeah. Absolutely. That’s effectively what our process is. We basically have about 1,000 manually curated inputs globally that we think are both theoretically leading, and also when you look at the data, the data revisions are quite low in real time. They release monthly, right? It tends to be high-quality data. So that’s the kind of manual part where we do the curation. After that, then you throw it to the model, and the model tells you, “Okay, at this point in time, this thing matters more.”

So yeah, I don’t think it’s like a fixed answer, but the idea is that we clearly have to acknowledge the economy shifting, you know, manufacturing versus services, right? Like online data. Another classic example is consumer sentiment, right? Consumer expectations, that’s become pretty much useless over time as things have shifted. But there’s alternative measures for the consumer that give you a good read, right?

And I would argue one of the reasons people might think it’s stopped working is because we’ve lived in this post-pandemic price level as well as inflation kind of environment, where it’s not just the rate of inflation, but the fact price levels reset higher, wages haven’t kept up, and so consumers are telling you they’re struggling with real incomes, right? But historically, because we’ve gone through a long period where there hasn’t been a lot of inflation, historically that series was very correlated with job market prospects. So again, when it was correlated with job markets, it makes sense why that gave you a very good lead on growth, right? Now that it’s all about inflation, there tends to be more second-order impacts on why that series moving is no longer a leading indicator directly for growth, right?

Jack: So you guys boil all this down to a macro risk indicator, which I think is very cool. And we’re gonna put up a chart here of the macro risk indicator. So can you explain, first before we talk about what it’s telling us now, can you explain how you construct this?

Tian: Yeah. So we came up with this idea because we wanted to abstract macro down for people that don’t want to think about macro all the time, right? Macro sort of doesn’t matter until it does. Like once every five years, suddenly it matters, kind of thing. And so that was kind of the original idea. How can we boil it down to be something as simple as possible but no simpler?

So we essentially came up with these four dimensions of macro, which is growth, inflation—the traditional kind of Bridgewater style framework—and then we added in policy and liquidity as the four. And essentially how we come up with these components is they’re actually decision trees that our model essentially goes down. So it’ll check, say, “Hey, what’s the US growth indicator doing right now? Okay, given this is going up, what’s China’s growth indicator doing now? And given China’s growth indicator is going up, then what’s Fed policy doing now? Oh, the Fed is neutral and the growth is doing good, then that’s likely to give me a point for risk on.”

And it’ll go for a lot of these decision trees to come up with a final score. So it’s an attempt to formalize and make repeatable some of these relationships in macro that are intuitive, but you have to kind of do them all at once to get understanding of the outlook. So that’s kind of how it’s built, but essentially it gives you a number between zero and 100. It moves smoothly over time, and the idea is to use this to dial up or dial down your risk exposure.

Jack: So this has been correctly risk on for a long time now. Can you just talk about what this is telling us right now?

Tian: Yeah. It’s still basically in the risk on regime. It’s been saying essentially since even around when the war started that growth is basically resilient. It thinks the policy risk is a bit overstated, so it kind of sees the policy landscape as pretty bifurcated. You’ll have very obviously hawkish global economies and central banks like Japan or Korea, but it thinks a lot of Europe shouldn’t be as hawkish, for example. And it thinks that it’s touch and go for the Fed, right? The Fed shouldn’t really need to be hiking.

So it’s just combining a lot of these factors. And right now the overall risk on message is because it thinks policy is pretty neutral to slightly risk on. It thinks inflation is a problem, but not overly so. It thinks growth is fine, and it thinks the broader liquidity environment is good. And so when you piece it together, that’s why it’s risk on. And in a way, if you just look at the fact that equity markets are broadening out during the semi drawdown, right? That gives you a good clue on liquidity probably isn’t as tight as everyone says it is.

Jack: One of the things a lot of people have been talking about is building headwinds, but you’ve kind of taken that the opposite way. You’ve talked about not necessarily building headwinds, but tailwinds that maybe are getting a little bit weaker. Can you talk about that?

Tian: Yeah. Well, I think that reflects the fact our model has been going from super risk-on in the last year to now being slightly less risk-on as prices have adjusted higher.

The thing I think about as a mental model is that markets are very efficient at pricing first-order impacts, right? So when things happen, markets and people get a handle on it pretty quickly. And I think this is the underlying rationale behind very famous quips in the market. Like Bob Farrell has one of my favorite quotes about when all the experts agree, something else happens, right? And I think that’s an attempt to get at this idea of everybody pricing the first-order impact.

But then what’s interesting is once everyone prices it in, it’s not necessarily wrong, right? The key is, once we all realize, do those first-order impacts have some kind of second-order impact, which is usually a shift in policy that happens. And so that’s the way I think about using this, right? This is giving us a sense of, hey, first-order impact things are risk-on good. That’s how people are positioned. But then given this outlook, do you think policymakers want to lean against it? And are they gonna shift policy or not?

And I would say right now it’s kind of like, yeah, I don’t see why policymakers want to massively lean against the fact we have an equity bull market, economy is fine, right? They’re not actively trying to do something against it. So then the model’s probably valid. There’s no second-order impact resulting, therefore the outlook’s broadly good. We’re still staying fully invested in equities.

Jack: One of the things we’ve been talking about a lot on the podcast is this idea that AI CapEx is dominating everything. It’s definitely dominating the market, and some argue it is or is not dominating the economy. But if we look at the overall economy and everything that’s going on, how important is this AI CapEx right now?

Tian: Yeah, it’s very important, for sure. The way we think about it is we use the Kalecki-Levy framework quite heavily actually. So the basic concept is one person’s spending is somebody else’s income. So the way the economy functions is, as long as I spend, that creates income for someone else, and then they’re more likely to spend. And so the inverse of that is obviously tracking the savings rate. So as long as people are drawing down savings and dissaving, that on net creates a lot of growth and a lot of resilience.

So we basically have an environment where corporates are dissaving at a historically epic rate, are investing a lot more, and at the same time, US households also are maintaining a very low savings rate. So if you have an environment where both households and corporates are choosing to spend more, save less, then that’s income for somebody else, right? And it just keeps flowing around, and I think that’s what’s been helping keep the economy very resilient.

The time to worry is precisely when these hyperscalers or people suddenly dial back their CapEx, right? And suddenly showing that at the margin they wanna save more. And if they wanna save more, that’s gonna be less income for someone else, and suddenly that whole loop can potentially start to unwind. So yeah, it’s been very important, but it’s one piece alongside the household piece.

Jack: Yeah. I think the thing I’ve been thinking about a lot is the downstream benefit of this, like the end user ROI of CapEx and how important that is. And it seems like maybe the market’s not that concerned about that yet ‘cause we’re a little bit down the road, but do you have any thoughts on that? Like how important it is that we start to see ROI from this CapEx downstream?

Tian: Yeah. So I would say there’s a theoretical and a practical answer, and the practical answer is, it’s very hard to measure ROI in real time, right? We’re not gonna be able to measure this in real time. So it’s not like there’s gonna be a smoking gun you can point to which is like, “Oh yeah, this definitively proves AI is not creating any value because these companies make no profit,” or vice versa.

So I think it’s more about thinking about the broader kind of sequencing on how these things play out. That’s more of the way I would think about it. So as of right now, we actually think we’re somewhat following the kind of mid-2000s playbook when you first had the initial dot-com bubble that led to all that investment, and then it started to slowly diffuse out into the economy and start to boost labor productivity. We actually think we’re seeing the first signs of that at the margin. Again, not a huge amount, but these things are obviously tough to measure in real time. I actually think there’s a good chance it will start to diffuse out.

The reason investors are concerned is because ultimately everybody agrees this is game changing. The problem is who actually makes a profit in the profit pool. And I think ultimately that’s where the sequencing comes in, where right now the kind of bottleneck hardware leg of the trade is done, right? Everybody realized these were the bottleneck, they’re gonna benefit, and all the money crowded into there, and that’s what’s gone up.

I think this next phase is potentially you’re actually into more the Jevons paradox leg of it, where suddenly costs come down, people use it more, the AI adopters start to benefit. So the profit pool starts broadening out into those guys who benefit, the hyperscalers suddenly start taking advantage, right? I would say that’s one dynamic that’s kicking in. And that’s gonna carry on until we get another technological breakthrough, right?

Just like at the end of ‘25, you could have made all the same arguments—man, this CapEx, or the bubble’s over—and then suddenly agentic AI is a thing and it sets off a whole other leg with narratives. And so right now it’s not clear what the next leg is, but maybe if we get to recursive self-improvement or world modeling or something else suddenly comes through that’s a big thing, suddenly everyone reassesses. That might be the next leg. But until you see that, I would say you’re in that phase of the AI adopters starting to benefit until you get to the next big step change in demand for compute, right?

Jack: Yeah, it seems like it makes it so hard to predict because that next technological breakthrough, you don’t see it coming, but then when it does, it could be a massive game changer. This technology is moving so fast, it seems like it’s hard to predict the whole thing.

Tian: Yeah. Which is why I think it’s important to be adaptive and open-minded. So that’s why I understand the major concern. There’s a lot of very smart, good strategists talking about this is the railway bubble, right? Or the dot-com. Like there’s a lot of that. And I’m just saying, okay, I would like to understand what their argument is, which is a classic capital cycle over-investment argument, right? But now I wanna also be open-minded about, okay, what’s the falsifying thing about it?

And there’s a couple of things I think that make this cycle a bit different, right? First of all, this is not just a private investment boom cycle funded by private sector credit, right? This is a sovereign existential race between the US and China where the state is gonna be behind it. So you’ve gotta almost think of it as national balance sheets, right? Just like Chinese companies, Chinese government—people don’t have a lot of trouble thinking about that as one thing. It’s all coordinated and then they’re deploying money into AI and tech.

But I think you can get somewhere similar with the US. Mag Seven, US government, that can be a lot of coordination, a lot of private-public initiatives because it’s a very important technology to win at, right? And these are the things that can potentially elongate the cycle more than just purely looking at traditional private credit metrics. So I just think there’s a lot of these forces at play that mean it’s very hard to be declarative, right?

Like, you know, Howard Marks has that very famous quote about, it’s always what you know for sure that just ain’t true, that’s what destroys your portfolio, right? It’s not so much what you don’t know. And a lot of the takes on AI—I feel like people veer from one to the other, right? It’s like this is definitely a bubble. This is gonna destroy all capital. Or suddenly it’s the greatest thing ever and, oh my God, it’s gonna grow to the sky. I feel like you’ve seen that pretty aggressively this year as people shift their mindset.

Jack: To that point, how do you think about the long-term benefits of this? I mean, you’ve got people in the tech community that are talking about deflationary growth and the world of abundance this is gonna create, and levels of GDP growth we’ve never seen before. And then you’ve got other people who say, if you look at the history of these technologies, GDP growth usually ends up about the same thing. Like in the long run it doesn’t change that much. I mean, is that even a question worth thinking about, or how would you think about that?

Tian: Yeah. As you say, I would generally put it in the “too hard” bucket or “not really practically relevant for investors” bucket, right? Like we can debate about, hey, this is like electrification, it can boost productivity 1% over the next 50 years, right? I don’t think those are very practical things for you as an investor today on what to do.

However, if you think it through, though, when we look at these historical cycles that you mentioned, a very common pattern is this idea of complementary assets, right? So ultimately, the way humans and systems function does not change overnight, so it’s very hard for our preexisting systems to fully utilize these new technologies. It takes a bit of time. And so often during that transition, it’s the so-called complementary asset that wins.

And this is where your Amazon, Microsoft, Googles come in, right? Because they have your trust as your enterprise partner, your data’s with them. You trust them to be safe. You trust them to take care of your privacy, your data security, all these things, right? And then suddenly, if AI is a great technology, you’re still gonna potentially use them to leverage AI for your own needs, right? And so that’s a good example of these core businesses that didn’t necessarily invent the new technology, but who has the main complementary asset that allows the technology to diffuse more widely and be adopted, they tend to win.

So complementary asset is one that works really well, and the other one is always data. Like historically, no matter what kind of boom bust cycles you’ve seen in tech, the data part, we tend to just keep collecting more data, store more data, and data tends to go up.

Jack: I wanna pull up a chart. We talked about recession earlier and the fact that some of these things have been predicting recession and we haven’t seen it. I wanna pull up a chart from one of your presentations ‘cause you guys have so many amazing charts. And one of these is this idea that you talked about dissaving, and a lot of people are talking about dissaving as kind of a risk. But you have this chart that says recessions are usually preceded by a rising savings rate, not a falling savings rate. So could you talk about that and why that’s true?

Tian: Yeah. This is where a micro phenomenon does not necessarily work at a macro level, right? ‘Cause obviously for people that save more at an individual level, it’s generally considered like the good, right? Because you live within your means, you have resilience. But as I said before, when you choose to spend less, there’s somebody else who has less income. So at a macro level, if we all try to save more at the same time, the second order impact is all our incomes go down. Suddenly if our incomes go down, we’re gonna have to decide, are we gonna save more or borrow more? And if we choose to save more, that sets off this negative feedback loop.

And even visually, the way I present the chart, obviously since COVID, I have to do log scales now because of the distortion. But you can kinda see this pattern that, yeah, in the lead up into when recessions have started, you’ve generally seen precautionary savings pick up, and that initial pickup in precautionary savings is what starts to draw down income for everybody else, and that’s what potentially sets off the kind of economic slowdown. And obviously if it gets really bad, it goes into a recession.

Jack: As you mentioned before, we’re gonna have people probably calling the market top for a long time before we actually have a market top. But one of the things you guys have done that’s really cool is you have a market tops checklist where you’ve looked at some previous market tops, and you’ve looked for some things that are in common, and you look through that checklist as you evaluate things like that. So could you talk about what’s on that checklist?

Tian: Oh, yeah. I mean, I can send you an updated one. It’s a very long list, but the basic concept is there’ll be behavioral signs you typically see at the top. And I think that’s what everyone’s obsessed with right now, circular financing. Obviously, we see the crazy leverage, retail getting hype. That’s definitely one aspect. You know, famous investors, right? Guru investors. Obviously, we’re recording this literally the week after situational awareness made headlines, right? So that’s definitely one piece at major market tops.

But what you also tend to see is the economic piece, right? The growth slows down, the policy tightens, liquidity tightens, and that’s the macro piece that we wanted to flag. That if you go back to things like 1972, the Nifty Fifty top, you go back to even 1929, these major historical tops, dot-com. It’s not just that things look excessive and crazy and stupid. There has to be a mechanism in which the crazy stupidity ends, right?

And the mechanism has typically been monetary policy tightening for about six to nine months, right? Where central banks will, by however many means, at the margin liquidity starts to tighten, which starts to hurt people’s ability to borrow. So margin debt and these... You know, every cycle is called something different, right? Leverage starts to go down. And then what you see is, though even though leverage is going down, the concept stocks still rally, right? And then that will be a sign of rotation. People start selling down everything else in their portfolio to buy the concept stocks because they only ever go up, right? And even though liquidity is tightening, they’re still doing that. You’ve actually tended to see that at the major generational tops.

And I think that’s what’s different about today. Today, the average stock is actually making higher highs, higher lows, right? We would define that as tracking an index like Value Line arithmetic, right? You can just see it. It’s just going higher high, higher low. So yeah, those are the typical things you wanna see. It’s not just the signs of excess. You need to see signs the economy is gonna slow, liquidity is coming out. And obviously valuations are high, but I think we can generally accept valuations are pretty elevated.

So you typically need to see all of that. But again, it’s the sequencing. Often if all these things are in place, it’s more a measure of gravitational energy in a way, right? But to turn into kinetic energy, normally there’s a mechanism, and it’s almost always something about policy that forces a kind of cash settlement into the market, right? There’s always some kind of major event. Like at the dot-com top, it might be the AOL Time Warner merger, right? Some big event that has to be settled, and then that settlement sets off a bunch of activities.

Obviously, this year we had a mini run with the SpaceX IPO, right? Now the unlocks are coming through, so all the people that have SpaceX now need cash. They need to liquidate into the market, right? So that’s the first test. And then when Anthropic comes to IPO in October, whenever, when OpenAI comes to IPO, end of year, those are gonna be real tests. That’s cash settlement in an environment where you have to see people willing to actually settle with cash, right? It’s very easy to do stock for stock deals or do payment in kind or like, “Hey, I pledge you compute,” or, “I backstop your loan.” The test is always you gotta get to the point where cash settlement needs to kick in. And I would say that’s the thing to be on the lookout for.

Jack: So are you not seeing too many of those signs, though? Not the triggering event itself, but the actual signs of a market top. Are you not seeing a lot of those right now?

Tian: Yeah. I would say it’s like amber warning, right? Like as I say, the behavioral excesses obviously is there. The valuations obviously there. But the economy, like we talked about, is kind of still okay, right? It hasn’t quite slowed down. Liquidity hasn’t really tightened too much. And these cash settlement forcing mechanisms, we’re only just seeing the beginning of it, right? But you’re still seeing companies announce mergers and deals all the time, right? “Hey, I’m gonna use my expensive stock to go buy up your cheaper stock.” And you’re seeing a lot more of those things and they’re going through, right? Like you need to see these deals start to fail and then that’ll be a pretty big warning sign. But so far we haven’t quite had that.

Jack: You mentioned SpaceX and Anthropic and OpenAI potentially coming as well. How do you think about that? Like a lot of people think about this idea of a lot of supply coming on as sort of a major issue for the market. And we’ve had some guests who say, “You know, no. It’s not. The market can absorb it. It’s not gonna be that big of a deal.” How do you think that through?

Tian: Yeah, so from first principles, the way I think about it is when... Say you hold SpaceX shares, right? And suddenly you’ve decided, okay, the unlocks happened. I’ve made my 10, 100, 1,000X, whatever you bought in, and you sell. What do you do with the cash? So if I take the cash and I’m like, “Oh, you know what? I’m gonna just park it in S&P index funds, or I’m keeping it in Nasdaq funds,” then obviously that’s fine, right? ‘Cause that money is going back into the market and it’s broadly participating.

But what if I’m just like, “You know what? I don’t like public markets. I’m gonna take my money and go do something else with it. I’m gonna reinvest it back in private, or I’m gonna sit on it”? Right? If a lot of SpaceX holders start to do that, then suddenly it is a net supply. But to your point, if after these IPOs the money ends up being recycled into the wealth management or whatever—you know, Goldman called up all these guys and signed them up to their wealth management service, and suddenly the money goes in and gets placed into Goldman’s whatever strategy and goes back in the market—then obviously the market hasn’t had to absorb it and liquidate cash.

So I think that’s the kind of unknown part. My suspicion would be there is some net supply into the market ‘cause I don’t think the people cashing out are just gonna blindly put it all back into listed equities, right? If anything, I’m under the impression people that have done well in private markets are probably gonna keep it in private markets, which means there’s a net supply the public markets have to absorb, and that would obviously start to add a bit of weight on top of the index.

Jack: You guys had another really interesting chart in one of your papers here. This chart here I’m gonna throw up, which is this crowding score, 10-year percentile against capital cycle score, and you’re looking at a bunch of different industries here and where they stand. Can you just talk about what this system is and what it’s doing?

Tian: Yeah. So our single stock framework generally has a few pillars. The most important structural pillar is the idea of the capital cycle. That again goes all the way back to Marathon Asset Management. Edward Chancellor wrote the brilliant original book on it. And what we’ve tried to do is take inspiration from that and quantify and build our own scores.

So we essentially try to understand which of the sectors are seeing over- or under-investment relative to the operational ROIC generated. So on our model, semis have actually remained relatively capital scarce despite all the hype in the area, because what’s been happening is that they have not themselves over-invested yet, right? Even at the margin, if you see the memory guys, they’re only just starting to bring up CapEx, but they’ve made phenomenal returns. The areas of over-investment has been their customers. It’s your hyperscalers. It’s people like that, that have actually gone out, massively increased their CapEx to buy the products from them. But at the same time, these guys have not really generated the same returns on that CapEx so far.

So that’s essentially what the capital cycle score is doing, is trying to take all the different global profit pools, trying to understand within that sector, that profit pool, what’s been net investment and what’s been the return generated, and ranks everything. And that gives you a long/short profile.

And then for crowding, it’s basically built on top of the standard crowding. So people have 13Fs, right? All the filings and things like that. Except we have a couple of higher frequency crowding metrics. So we have a fast money proxy and things like that, that we blend together to give you an overall sense of how are institutional managers, speculative money, how are they positioned overall. So obviously the sweet spot is that you ideally want to buy companies that are good on capital cycle but low on crowding, right? And that’s kind of, over time, a repeatable way to screen for ideas.

Jack: What do you think about these arguments? You mentioned semis. We’ve had a lot of people talking about this idea that semis aren’t cyclical anymore. That, you know, they’ve got such a tailwind from this AI thing, that semis have traditionally always been cyclical. And when they look kinda like they look right now has not necessarily been a great time to own them historically, but some people are arguing that this is really not a good time to own them, and some people are arguing that the world has changed, these aren’t cyclical companies anymore. What do you think about that?

Tian: So I guess one of the mental models I think about a lot in investing is this idea of a diffusion of information, like an S-curve, right? Like, all the best investments, ideally you don’t have to be the super early adopter, but you kinda catch it when the idea goes mainstream, and you just ride it until it’s super late stage, where your cousin who’s got nothing to do with finance calls you up asking about it. And obviously we’ve seen that constantly in Bitcoin, gold, semis, right? All these things. This S-curve diffusion.

So I guess I don’t actually have that strong an opinion on it. I’m just more like I think this narrative that semis are no longer cyclical is definitely somewhere in the mainstream part. I would say there was way more skepticism beginning of the year, and that narrative’s gotten a lot more hold, so it’s somewhere from the middle to the late part, right? There’s obviously certain people, I look at them like they’re very steadfast, this is cyclical, this is all ending in tears. If you just see one or two of them give up, that’s probably a sign that it’s very late stage. So that’s more the way I would think about it because it’s a slightly academic debate, right? It’s really hard to know because this is a generational investment cycle. And like I say, with all the sovereignty angle over the top, right? It’s actually not straightforward. So my mindset is more like, how widespread is the narrative? If enough people think about it, then it’s probably priced in.

Jack: Yeah. It’s like when the skeptics initially appear, that’s still pretty bullish for the whole thing. But when it becomes like everybody just accepts that they’re no longer cyclical anymore, then you’ve probably got a problem on your hands.

Tian: Yeah.

Jack: It feels like we’re getting closer to that than not, right? Like there’s way more talk about it’s not as cyclical.

Tian: Yeah.

Jack: How are you thinking about inflation right now? That’s something we’ve been talking about a lot on the podcast. And inflation’s been above target for a really long time right now. I mean, the Fed obviously hasn’t hiked yet. Some people think that the odds are above 50/50 now for September that they might. How do you think about inflation? Do you see it as a big problem right now?

Tian: So the short answer, we don’t think it’s being a big problem. We obviously understand it’s gonna be above target and our headline inflation LEIs have been surging, right? So our headline inflation LEIs are saying, “Hey, inflation’s gonna be north of 4%,” right? But I’m not worried about it. And I think that’s the gap that there’s been, and I would say I’ve got a lot of pushback with our clients and people I talk to on this.

So one thing we noticed is there’s a meaningful divergence between headline and core inflation. Headline inflation is mechanically a reflection of the fact we have energy prices, right? And that’s basically driven it. So if the Iran war deescalates or the impulse shifts, that will go away. The underlying core inflation hasn’t been super strong in our opinion, because core inflation is ultimately driven by housing, by the labor market, and just by normal activity, small business, lower income consumers.

And on all those, the picture’s a bit more subdued, right? US housing, obviously transaction activity has slowed down. Mortgage rates are back up, right? There’s things that keep a lid on housing. House prices aren’t really going up that much. So you don’t really have the inflation piece from housing.

On the labor market, we think the US unemployment rate is roughly around where the natural rate is. So even theoretically, there’s not a huge amount of wage pressure, especially with AI and things like that. And even in the data, nominal wage growth is generally trending down. So again, you don’t have a huge amount of second-order wage pressures built up. And then we look at things like small business surveys, right? Things like NFIB surveys. There’s not much intention to raise prices or raise wages from these businesses. So broadly, I would say most of the underlying things are muted.

A very simple rule of thumb I try and describe is the idea that if you think about the K-shaped consumer, the lower half consumers matter for inflation. If lower half consumers are doing well and they have real income growth, that means companies are gonna pass on price increases to them. So that’s gonna allow inflation to go higher. But upper half income consumers tend to matter for growth because obviously they’re a bigger piece and they drive the overall growth number.

So I think we’ve had this pretty bifurcated dystopian kind of US economy for a long time, where your K-shaped consumer gets you into a sweet spot—but like the not-good-for-society kind of sweet spot—where because lower income wage growth is poor, right, and lower income finances are so poor, they can’t really absorb that much price increases from here, so you don’t get a huge amount of inflation pressure. It’s the same reason you see things like New York Fed surveys on delinquency rate, right? And those things are very high. But at the same time, you haven’t had a recession because the upper half of the K-shaped consumer’s been fine.

And I still think that’s the underlying dynamic. So yes, we’re mechanically above target, but it’s a classic kind of supply shock that policymakers should look through, and the underlying housing, labor market, small business, lower income consumers, they don’t suggest there’s that much upside inflation risk.

Jack: So do you think to some extent you have to ignore oil a little bit in a situation like this where one tweet can change oil or one development in the war can change oil so much? And that seems to have a big effect on inflation overall. Do you have to sort of pay a little bit less attention to that and try to get at the underlying drivers of what’s going on?

Tian: Yeah. It’s obviously difficult because of just how volatile it’s become. But in general, you would need oil to average a high level for a while. So just because it touches a level is not necessarily the same thing. For sure, in terms of base effects, it’s gonna have some pass-through. But these are the classic things that if it’s a supply shock, policymakers should be looking through, right?

I think the problem is because COVID and the post-COVID almost double-digit inflation is so fresh in everyone’s memory, and it was such a big failure of central banks and central bank credibility, that all the central banks are gonna default almost more hawkish than they would’ve needed to be now because they’re very worried about credibility. So I think that’s probably more the impact it’s had, that they’re gonna have to talk super hawkish. But in terms of following through and hiking, my view is the bar is pretty high, right? Because again, we live in a world where politics trumps economics, right? There’s just a lot of other factors. That means policymakers, everyone’s not free to act in the same way they did 20 years ago. So yeah, clearly mechanically there’s an impact, but I think we’re of the mindset it’s probably not macro relevant for inflation. It’s gonna be looked through.

Jack: You mentioned the labor market, and you have a great chart, this US consumer personal finances versus jobs hard to get, which is the Conference Board Jobs Hard to Get indicator. Can you talk about what you’re doing with this chart and why you think it’s important?

Tian: Yeah. I think we hit on it a little bit earlier in the conversation. I’m trying to showcase visually the underlying state of the US household and US consumers. So what’s very interesting if you look is historically they’re very well correlated, right? Through multiple cycles. Because in general, your personal finances are related to your ability to get a job and to your incomes.

And what’s been very interesting is that after COVID, this completely diverged, where most of the survey responses are telling you it’s easy to get a job and they’re not too worried about getting a job. Yet they continue to tell you personal finances are struggling. And ultimately, that gap is explained by price levels, right? The massive reset in price levels and the inability of real income levels to keep up with the cost of living.

So I think this is a really stark way to understand the cost of living pressures, especially for low-income consumers right now. And obviously, there’s multiple ways to measure it, right? Like amount of people working multiple jobs and all these things. So it’s just telling you, yes, people can find a job, but it’s not the best job. It doesn’t pay you enough to sustain the previous standard of living people expect. And this gap is obviously unprecedented in the history of the data.

Jack: I want to shift to the Fed because it’s something we’ve been talking about a lot. We obviously just had a change in the Fed chair. And one of the things I found in researching you is I believe your out of consensus view here is that you do not believe the Fed is going to hike this year. So can you talk about the position the Fed’s in and what you think they might do?

Tian: Yeah. So first of all, I will have to say one of the implicit assumptions clearly is that the energy shock will dissipate, the impulse will go down, right? So by the time you get to September and later in the year, there’s some leeway.

But I guess the fundamental thing I’m driving at is I acknowledge if this was a normal monetary policy cycle and the central banks are supposed to look at growth and inflation and try and meet their targets, they should be hiking. Right? I get that. But going back to my earlier comment on sovereignty and politics, I think they’re way more important in the current investing landscape.

And so the way I would read it is I think Warsh is gonna be incredibly important in the history of the US for doing reforms, right? And you have to ultimately tie together him and Scott Bessent, right? Obviously, they worked together for Stanley Druckenmiller previously. I think they’ve all generally been consistent in recognizing that there’s been some very bad externalities from the post-GFC policies from the Fed, right? In creating extreme wealth inequality, in introducing massive moral hazard, disabling the function of the money market, right? To give signal, flooding the system with reserves and all these issues.

And I think Warsh is coming in to try and genuinely get a handle on this and reform things, right? And if you look at the task forces, the people he’s picked, I think that’s actually long-term extremely bullish, extremely good, and that’s ultimately gonna be very important for credibility.

Now, to do that, there’s gonna be a lot of pushback. You’re gonna have to spend a lot of political capital. So then the question is, do you wanna waste your political capital for 25 bps here or there in some of your first meetings when you know for sure the president, President Trump, doesn’t want you to hike? That probably one of the behind the scenes trade-offs is that he probably assured him he’s very unlikely to hike when he was getting the job.

So then you have to ask, is the data so bad that he can justify to President Trump why they hiked? If not, then he should just err on the side of not doing it. Don’t bring attention to yourself. Get your things in place. These reforms are a multi-month, if not multi-year process. That’s absolutely vital. And you don’t want to take risk getting derailed from that for the sake of 25 bps here or there. Let the markets do what they may. Sure. Market can flatten, steepen the curve after the meeting. Hey, if you think the Fed needs to hike more later, great. But I don’t think that’s their goal. That’s not the most important thing. That’s just where we’re coming from.

Jack: What do you think are some of the most important changes we might see under Warsh? I mean, people talk about less forward guidance. They talk about less use of the balance sheet. What do you think are some of the major reforms or changes we might see under him?

Tian: Yeah. I think it’s gonna be extremely difficult, but they need to try and get the system away from the excess reserve regime that central banks have operated in, right? Because it does create a lot of distortion in terms of how capital is allocated, right? If you’re a central bank, you just mechanically let banks run huge reserve balances. You pay them interest. You’re just giving out money all the time, right? And so you create very different incentives for what financial institutions are doing.

So I think to the extent they can reverse that, that’ll be extremely important. But the challenge has clearly been when you’ve tried to do that, markets crash because of this moral hazard post-GFC environment we’re in, so they’re gonna have to find a way to finesse it. And I think this is where viewing the Fed alongside Treasury, viewing Bessent, people who have an understanding of how markets work, how the economy works, but also how market participants think, and coordinating. I think that’s probably their best shot, right?

If they can do deregulation of the US banks at the same time, right? Shift the burden of credit creation, financing more into the private sector, but give the private sector some backstop along the way for now, but just transition that towards, say, smaller banks, right? Regional banks. You know, undo some of the worst parts of Basel III, right? I think there’s things they’re trying to do, but it’s gonna clearly take a lot of coordination.

You know, like we’ve seen with how masterfully Bessent managed his dollar-yen intervention, right? It’s just, you know, in a way it’s almost ridiculous. He has that notepad where he’s like, “To do: Buy yen,” and just make sure you guys see it, right? So you can see that I think you’re in the hands of people who have a genuine understanding of how market participants operate, and that potentially gives them the shot of pulling this off.

Jack: Yeah. It seems like the challenge is always everybody has big plans, and then you just don’t know what they’re gonna do when they’re punched in the mouth, basically. Kind of like the Mike Tyson quote. Like, that’s the challenge, is will they be able to get through whatever goes wrong and stay the course?

Tian: Yeah, it’s hard, right? Because the history is against them. But I think you have a better shot if you know how participants think, right? I mean, we saw it in a way after Iran with oil, right? I mean, it’s almost masterful in that they’re like, “Okay, let’s just create so much two-way risk that all these levered guys get stopped out both sides.” And then eventually nobody... You just create P&L loss, and suddenly nobody wants to speculate on the market, right? And you just force that risk down, and it doesn’t move as much.

In a way, that’s kind of almost what they’re doing on dollar/yen, right? You just create so much two-way risk, you just stop everyone out. That buys you some time. So I think those are probably signs of nuanced understanding of market operations that gives them an extra quiver to their bow that maybe historically policymakers have not had or have not been willing to use. But today they’re willing to use it, yeah.

Jack: This next chart is the number of US states meeting the Sahm rule. That currently sits around nine, but I’m just wondering if you could talk about, maybe for people who don’t know, just define what the Sahm rule is and then talk about what this chart means.

Tian: Yeah. So this is named after Claudia Sahm, a very prominent economist who came up with this as a proxy for recessions. And so she did this for the whole economy, but the general idea was, if you see the unemployment rate rise like, I think it’s like 50 bps off a three-year low or off the lows, in a short space of time, that’s usually a sign of a recession. And historically, it’s been almost a perfect hit rate, right? For the economy as a whole. Very famously, this rule failed along with many, many recession rules in ‘23, ‘24. And again, I think there’s a number of reasons that happened, but ultimately the simple answer is fiscal, right? We were in a new fiscal dominance regime that invalidated a lot of these indicators.

And so what we wanted to do was at least take that but apply it to the different US states to give you a sense of the underlying picture because, you know, this idea of bifurcated K shape is everywhere, right? Like the economy, everything is getting bifurcated in K shape. So by breaking it out, you get a sense of how things are doing in terms of the breadth, not just the aggregate number.

So this does show you that for the US economy, there’s still a decent amount of stress in certain states that have not necessarily participated in AI or in the energy boom, right? And so that also feeds into things like consumer sentiment staying weak, right? That feeds into political pressures. And so these things are all somewhat linked. But I think the point of this chart was to say, yeah, the labor market is not obviously booming. It’s just very bifurcated.

Jack: It seems like that word bifurcated explains so much about what’s going on. You know, you’ve got people who have a lot of money versus people who don’t. You’ve got tech versus everything else in the economy. It seems like there’s been so many bifurcations in what we’re seeing in recent years.

Tian: Yeah. And I think it’s only gonna get more so because the market will keep being more distorted because we live in a world where governments are gonna actively intervene, right? Whether you wanna call it industrial policy or things in the name of national security, right? There’s just tons of these things that will keep happening, and they’re gonna drive a lot of divergences and create winners and losers that a free market normally would find a way to digest and normalize, but there’s just gonna be so many more of these things that make it harder for these bifurcations to close on their own.

Jack: You know, I think AI too. I mean, I don’t know if you agree, but I would think AI would also cause more bifurcations.

Tian: Yeah, like until you get a policy intervention, right? Like, famously in Korea, they’re talking about finding ways to tax it to distribute the gains more widely. Obviously, that caused the market to sell off. So then they’re like, “Okay, maybe we have a sovereign wealth fund invest.” But these are all gonna be, I think, questions that societies will have to grasp and deal with. But yeah, if you have these increasing returns to scale winner-take-all technologies, then it’s obviously inevitable that if you leave them to their own devices, they’re gonna create extreme winners and losers.

Jack: On the point of AI, how do you think about AI in terms of the job market? I’ll put up this chart you have, US Challenger layoff job cuts by reason. And AI is one of the reasons you list there. But, you know, you have some people thinking that even if this is a technology that’s gonna change the world in many ways, we’ve got a pretty rough ride to get there in terms of maybe some job loss along the way. How do you think about AI, I guess both short term and long term, in terms of its ability to create job loss?

Tian: So again, these are super difficult. I think there’s some very good work by Professor Bloom at Stanford that looks at long-term historical technology diffusions and the like. So if I were to just cite some of the things that I took from reading his work, I think basically these things take time is the first point, right? It’s rarely instant. There’s real world human friction and things that slow it down. So I think that’s usually the thing where these things are dragged out.

But in terms of even in our business, right, day-to-day, yeah, it’s pretty amazing, right? There’s certain things where I think it doesn’t necessarily force you to want to replace a lot of workers, but I think it reduces the value add for mediocre work that used to probably be more rewarded. And I think that’s what’s gonna be really challenging.

Again, if the market mechanism is allowed to operate, if you’re like, say, four and a half stars or four stars out of five at anything, I think you’re probably fine, right? You’re probably still gonna be okay. But previously, if you were like three, three and a half stars out of five or something, you used to be pretty integral to most organizations—competence, get things done. And at least so far, especially with agentic AI and these workflows, AI seems very good at giving you a three, three and a half job at everything. And I think that’s the labor market wage compression that it’s hitting.

So I think it’s gonna be a problem, right? And I don’t know what the answer is, but if you wanna look for signposts, things like youth employment’s clearly a big thing, right? You know, as we’re recording this, India has their whole cockroach movement, right? And you see this in lots of countries where youth employment... You’re basically gonna create lots of labor market pressures that will ultimately lead to more of a social political response one way or another. So again, I don’t know exactly how it plays out, but those are the things I would say you would look for, and most likely this leads to more kind of political shifts against capital is probably the ultimate end game.

Jack: Yeah, to your point, I think for people who are really good at what they do, I think it’s a huge leverage mechanism. You know, the more I’ve been using it, the more I learn. There’s just certain times where you have to interject and your personal knowledge leads to the AI being 20 times better. And so I think you’re right. I mean, I think for those people at the top who are really good at what they do and who have maybe some information that the AI doesn’t have, their ability to use it, I think it’s just gonna change the world in many different ways. And for the people who don’t use it, it’s probably a bad thing, I would say.

Tian: Yeah. Just more bifurcated. I agree with you. It’s coming. It is scary. Even in the last few months, the improvement is scary.

Jack: How much does it change what you guys do in terms of the types of research you do? I mean, has it changed it a lot?

Tian: So I think we’ve generally adopted a lot of different data science machine learning techniques anyway going back. So I would say it’s not like we’re forced to adopt it from brand new. But like you say, I think we’re in the mindset of man plus machine, right? Like you have a human AI loop. The creative aspect, I think you still probably need a human a little bit. Like just telling the AI on its own to go come up with something, it doesn’t seem as effective as giving it a more clearly defined task.

It’s been amazing at falsifying things. So it’s amazing at figuring out if there’s a bug in your code or there’s something you didn’t think about when you built a model, or you give it your thesis and you can test it and tear it apart, right? And see what you’re missing. That I think has been extremely good and productivity enhancing. But if I were to sit down from scratch like, “Hey, give me an idea,” and I don’t give it something, it can be a little bit tricky. So that’s probably more the stage we’re at.

I would say agents, especially internal agents using your own data and models, very powerful. Cowork, very powerful. And then the day-to-day is good, but if you’re doing it via browser or whatever, you do need to babysit it a little bit.

Jack: I wanted to ask about your VPX ETF because I think that takes a lot of the things we’ve talked about today, your framework and how you think about markets and the economy, and it translates it down into an actual portfolio. So can you talk about how you build that portfolio, how you run VPX?

Tian: Yeah. So VPX is a long-only US large cap ETF. So we designed this to try to maximize upside capture and minimize downside capture to the S&P using the same stocks essentially. And the reason we thought we might have an edge in doing this is that a lot of traditional core allocation strategies are either obviously pure passive or they tend to be more static sector or factor allocations through time.

And I think one of our theses is that the world is now gonna change so much that a lot of these slightly more static tilts and factor shifts that worked in the past 10, 15 years might not work in the future. And so essentially what we’ve tried to do is combine our capital cycle, quality, crowding, our PPL, growth—all our models, macro, everything—to try and forecast forward returns for all the stocks and sectors, and then try to maximize theoretical returns, right? And so it’s a slightly different methodology to traditional kind of smart beta or factor investing. And so that was kind of the theory.

In terms of how it exactly works, essentially we use our capital cycle models to decide sector tilts, so which sectors are we over/underweight. And then within those sectors, we’ll use all the other single stock specific names to kick out the worst names, and then we’ll just hold the rest. So it’s very much this idea of addition by subtraction.

And ultimately, I just think of it as the product is for someone who’s like, “Hey, I have a lot of SPY or VTI in my portfolio, but I’m a little bit nervous at this point,” like, you know, half the index is in like 10 names or whatever it is. And, you know, the AI thing could carry on, maybe it carries on for another year, but maybe it’s over tomorrow, right? And then is there a way for me to just get long-term beta in a similar vehicle, but that tries to adapt? So that was kind of how we thought about it and why we launched the product.

Jack: So you mentioned you’ll kick out stocks. Will you kick out sectors as well? Or will you have all the sectors just at various weights?

Tian: Yeah, we will kick out sectors. So the market has a lot of obvious alternatives, so we take a lot of active risk if the model says it. So for example, when we launched in March this year, we ran an extremely large tech overweight. That would’ve probably made everyone pretty scared if you weren’t aware of the model. But then we rolled it up to May and then basically went underweight tech, and that was quite good for that market adjustment period. Right now we have a pretty big overweight on energy and semis again, right? So we’re trying to take a lot of active risk if the model says so, and then in periods when the model thinks there’s less divergence, obviously it will dial down the risk.

Jack: So what I like about that is we live in a world where people don’t want tracking error, where people are a little bit afraid to be different, and so it sounds like you guys are definitely, based on your conviction, willing to be different when it’s called for.

Tian: Yeah. I think that’s why we think that the product potentially has space, right? Obviously, if you want benchmark hugging, you can buy an index or something simple and you’ll be okay. I think this is more trying to take a lot of risk when you think it’s appropriate to try and maximize the upside and downside capture in a portfolio. So you don’t even have to sell all your SPY. It could be like, hey, you have $100 SPY, you could put $5 into this, 95 in SPY, but then it’ll try to improve the overall US core allocation’s upside downside capture.

Jack: Tian, this has been great. I really appreciate you taking the time. As we wrap up each episode, we have two standard closing questions we ask. The first is, what is one thing you believe about investing that the majority of your peers would disagree with?

Tian: So obviously it’s hard to define majority, but I would say we’re true believers in this idea that investing is as much about doing great fundamental analysis, right? Understanding how the world works. But equally, it’s as much about playing the game of investing. That a lot of times it’s understanding the players, the constraints, who are the buyers, who are the sellers, and I think doing both at the same time.

I would say this is obviously antithetical to like old school Buffett or buy and hold. But equally, I would say technical analysis would say, “Hey, why do all that fundamental analysis?” But I think we truly believe that there’s a heavy element of playing the game, but you can’t play the game unless you do your homework and actually understand what’s going on. So I don’t know if that’s a majority, but I would say that’s something that some people would definitely disagree with.

Jack: Yeah. No, I think a lot of people. That was a great answer. And our final closing question is, based on your experience in markets, what is the one lesson you would teach the average investor?

Tian: Yeah. So I think I hit on it a little bit with the Howard Marks quote earlier. It’s from his book, The Most Important Thing. I read that quite early on in my career, and I think it really did make a big difference to me. And there’s a couple of things he had in there, one of which was this idea of investing is like playing tennis, right? Except your goal is not to hit winners, but to reduce your own forced errors. And there’s a very similar kind of concept, I think, for long-term investing that average investors should think about.

And the other quote was something I said earlier, which was, “It’s not what you don’t know that destroys your portfolio,” right? It’s the thing you know for sure that isn’t true that destroys your portfolio. So I would say that’s probably the best thing to hold onto. Like, living to fight another day and creating your portfolio structure around that is underrated, right? Just like, obviously we’re recording this because of situational awareness. Even if he’s right, clearly he’s not gonna monetize as much as you would’ve now, right after this big drawdown and being stopped out.

Jack: Yeah. I feel like in these types of periods we’re in right now, people don’t think about living to fight another day as much as they maybe should. They think about maximizing their gains as much as possible, so that’s a really great lesson. If people wanna find out more about you, about your ETF, about what you guys do, where can they go?

Tian: Yeah. So the ETF ticker is VPX, so like, you know, S&P SPX, but VPX. In terms of our research and framework, variantperception.com, and we’re also on Twitter as well. You can find us there if you search for Variant Perception.

Jack: Well, this has been great. Thank you for joining us. We appreciate the time.

Tian: Yeah. Thank you for having me. Enjoyed it.